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Modeling Dynamic Environments with Scene Graph Memory Andrey Kurenkov, Michael Lingelbach,Tanmay Agarwal, Emily Jin, Chengshu Li, Ruohan Zhang, Li Fei-Fei, Jiajun Wu, Silvio Savarese, Roberto Martín-M
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Home Search this site Skip to main content Skip to navigation Modeling Dynamic Environments with Scene Graph Memory Andrey Kurenkov, Michael Lingelbach,Tanmay Agarwal , Emily Jin, Chengshu Li, Ruohan Zhang, Li Fei-Fei, Jiajun Wu, Silvio Savarese, Roberto Martín-Martín Arxiv | GitHub Abstract Embodied AI agents that search for objects in large environments such as households often need to make efficient decisions by predicting object locations based on partial information. We pose this as a new type of link prediction problem: link prediction on partially observable dynamic graphs . Our graph is a representation of a scene in which rooms and objects are nodes, and their relationships are encoded in the edges; only parts of the changing graph are known to the agent at each timestep. This partial observability poses a challenge to existing link prediction approaches, which we address. We propose a novel state representation -- Scene Graph Memory (SGM) --...
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